
import geopandas as gpd


# Read in the data
full_data = gpd.read_file("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/DEC_lands")

# View the first five rows of the data
full_data.head()


type(full_data)

geopandas.geodataframe.GeoDataFrame


data = full_data.loc[:, ["CLASS", "COUNTY", "geometry"]].copy()


# How many lands of each type are there?
data.CLASS.value_counts()

WILD FOREST                   965
INTENSIVE USE                 108
PRIMITIVE                      60
WILDERNESS                     52
ADMINISTRATIVE                 17
UNCLASSIFIED                    7
HISTORIC                        5
PRIMITIVE BICYCLE CORRIDOR      4
CANOE AREA                      1
Name: CLASS, dtype: int64


# Select lands that fall under the "WILD FOREST" or "WILDERNESS" category
wild_lands = data.loc[data.CLASS.isin(['WILD FOREST', 'WILDERNESS'])].copy()
wild_lands.head()


wild_lands.plot()

<AxesSubplot:>


# View the first five entries in the "geometry" column
wild_lands.geometry.head()

0    POLYGON ((486093.245 4635308.586, 486787.235 4...
1    POLYGON ((491931.514 4637416.256, 491305.424 4...
2    POLYGON ((486000.287 4635834.453, 485007.550 4...
3    POLYGON ((541716.775 4675243.268, 541217.579 4...
4    POLYGON ((583896.043 4909643.187, 583891.200 4...
Name: geometry, dtype: geometry


# Campsites in New York state (Point)
POI_data = gpd.read_file("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/DEC_pointsinterest/DEC_pointsinterest")
campsites = POI_data.loc[POI_data.ASSET=='PRIMITIVE CAMPSITE'].copy()

# Foot trails in New York state (LineString)
roads_trails = gpd.read_file("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/DEC_roadstrails/DEC_roadstrails")
trails = roads_trails.loc[roads_trails.ASSET=='FOOT TRAIL'].copy()

# County boundaries in New York state (Polygon)
counties = gpd.read_file("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/NY_county_boundaries/NY_county_boundaries")


# Define a base map with county boundaries
ax = counties.plot(figsize=(10,10), color='none', edgecolor='gainsboro', zorder=3)

# Add wild lands, campsites, and foot trails to the base map
wild_lands.plot(color='lightgreen', ax=ax)
campsites.plot(color='maroon', markersize=2, ax=ax)
trails.plot(color='black', markersize=1, ax=ax)

<AxesSubplot:>

#


import geopandas as gpd
import pandas as pd


# Load a GeoDataFrame containing regions in Ghana
regions = gpd.read_file("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/ghana/ghana/Regions")
print(regions.crs)

epsg:32630


# Create a DataFrame with health facilities in Ghana
facilities_df = pd.read_csv("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/ghana/ghana/health_facilities.csv")

# Convert the DataFrame to a GeoDataFrame
facilities = gpd.GeoDataFrame(facilities_df, geometry=gpd.points_from_xy(facilities_df.Longitude, facilities_df.Latitude))

# Set the coordinate reference system (CRS) to EPSG 4326
facilities.crs = {'init': 'epsg:4326'}

# View the first five rows of the GeoDataFrame
facilities.head()

/Users/db/opt/anaconda3/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
  in_crs_string = _prepare_from_proj_string(in_crs_string)


# Create a map
ax = regions.plot(figsize=(8,8), color='whitesmoke', linestyle=':', edgecolor='black')
facilities.to_crs(epsg=32630).plot(markersize=1, ax=ax)

<AxesSubplot:>


# The "Latitude" and "Longitude" columns are unchanged
facilities.to_crs(epsg=32630).head()


# In case the EPSG code is not available in GeoPandas, we can change the CRS with what's known as the "proj4 string" of the CRS. For instance, the proj4 string to convert to latitude/longitude coordinates is as follows:
#+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs


# Change the CRS to EPSG 4326
regions.to_crs("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs").head()


# Get the x-coordinate of each point
facilities.geometry.x.head()

0   -1.96317
1   -1.58592
2   -1.34982
3   -1.61098
4   -1.61098
dtype: float64


# Calculate the area (in square meters) of each polygon in the GeoDataFrame 
regions.loc[:, "AREA"] = regions.geometry.area / 10**6

print("Area of Ghana: {} square kilometers".format(regions.AREA.sum()))
print("CRS:", regions.crs)
regions.head()

Area of Ghana: 239584.5760055668 square kilometers
CRS: epsg:32630

#


import pandas as pd
import geopandas as gpd
import math


import folium
from folium import Choropleth, Circle, Marker
from folium.plugins import HeatMap, MarkerCluster


# Create a map
m_1 = folium.Map(location=[42.32,-71.0589], tiles='openstreetmap', zoom_start=10)

# Display the map
m_1


# Load the data
crimes = pd.read_csv("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/crimes-in-boston/crimes-in-boston/crime.csv", encoding='latin-1')

# Drop rows with missing locations
crimes.dropna(subset=['Lat', 'Long', 'DISTRICT'], inplace=True)

# Focus on major crimes in 2018
crimes = crimes[crimes.OFFENSE_CODE_GROUP.isin([
    'Larceny', 'Auto Theft', 'Robbery', 'Larceny From Motor Vehicle', 'Residential Burglary',
    'Simple Assault', 'Harassment', 'Ballistics', 'Aggravated Assault', 'Other Burglary', 
    'Arson', 'Commercial Burglary', 'HOME INVASION', 'Homicide', 'Criminal Harassment', 
    'Manslaughter'])]
crimes = crimes[crimes.YEAR>=2018]

# Print the first five rows of the table
crimes.head()


daytime_robberies = crimes[((crimes.OFFENSE_CODE_GROUP == 'Robbery') & \
                            (crimes.HOUR.isin(range(9,18))))]


# Create a map
m_2 = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=13)

# Add points to the map
for idx, row in daytime_robberies.iterrows():
    Marker([row['Lat'], row['Long']]).add_to(m_2)

# Display the map
m_2


# Create the map
m_3 = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=13)

# Add points to the map
mc = MarkerCluster()
for idx, row in daytime_robberies.iterrows():
    if not math.isnan(row['Long']) and not math.isnan(row['Lat']):
        mc.add_child(Marker([row['Lat'], row['Long']]))
m_3.add_child(mc)

# Display the map
m_3


# Create a base map
m_4 = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=13)

def color_producer(val):
    if val <= 12:
        return 'forestgreen'
    else:
        return 'darkred'

# Add a bubble map to the base map
for i in range(0,len(daytime_robberies)):
    Circle(
        location=[daytime_robberies.iloc[i]['Lat'], daytime_robberies.iloc[i]['Long']],
        radius=20,
        color=color_producer(daytime_robberies.iloc[i]['HOUR'])).add_to(m_4)

# Display the map
m_4


# Create a base map
m_5 = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=12)

# Add a heatmap to the base map
HeatMap(data=crimes[['Lat', 'Long']], radius=10).add_to(m_5)

# Display the map
m_5


# GeoDataFrame with geographical boundaries of Boston police districts
districts_full = gpd.read_file('/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/Police_Districts/Police_Districts/Police_Districts.shp')
districts = districts_full[["DISTRICT", "geometry"]].set_index("DISTRICT")
districts.head()


# Number of crimes in each police district
plot_dict = crimes.DISTRICT.value_counts()
plot_dict.head()

D4     2885
B2     2231
A1     2130
C11    1899
B3     1421
Name: DISTRICT, dtype: int64


# Create a base map
m_6 = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=12)

# Add a choropleth map to the base map
Choropleth(geo_data=districts.__geo_interface__, 
           data=plot_dict, 
           key_on="feature.id", 
           fill_color='YlGnBu', 
           legend_name='Major criminal incidents (Jan-Aug 2018)'
          ).add_to(m_6)

# Display the map
m_6

#


!pip install geopy==1.22.0

Requirement already satisfied: geopy==1.22.0 in ./opt/anaconda3/lib/python3.8/site-packages (1.22.0)
Requirement already satisfied: geographiclib<2,>=1.49 in ./opt/anaconda3/lib/python3.8/site-packages (from geopy==1.22.0) (1.52)


import pandas as pd
import geopandas as gpd
import numpy as np
import folium
from folium import Marker


from geopandas.tools import geocode


from geopy.exc import GeocoderTimedOut

def do_geocode(address, attempt=1, max_attempts=5):
    try:
        return geopy.geocode(address)
    except GeocoderTimedOut:
        if attempt <= max_attempts:
            return do_geocode(address, attempt=attempt+1)
        raise


result = geocode("University of Manchester, Manchester", provider="nominatim")
####(“University of Manchester”, “city=Manchester”, “type=University”)
result


point = result.geometry.iloc[0]
print("Latitude:", point.y)
print("Longitude:", point.x)

Latitude: 53.474855250000005
Longitude: -2.2336140230927333


universities = pd.read_csv("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/top_universities.csv")
universities.head()


def my_geocoder(row):
    try:
        point = geocode(row, provider='nominatim').geometry.iloc[0]
        return pd.Series({'Latitude': point.y, 'Longitude': point.x, 'geometry': point})
    except:
        return None

universities[['Latitude', 'Longitude', 'geometry']] = universities.apply(lambda x: my_geocoder(x['Name']), axis=1)

print("{}% of addresses were geocoded!".format(
    (1 - sum(np.isnan(universities["Latitude"])) / len(universities)) * 100))

# Drop universities that were not successfully geocoded
universities = universities.loc[~np.isnan(universities["Latitude"])]
universities = gpd.GeoDataFrame(universities, geometry=universities.geometry)
universities.crs = {'init': 'epsg:4326'}
universities.head()

95.0% of addresses were geocoded!

/Users/db/opt/anaconda3/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
  in_crs_string = _prepare_from_proj_string(in_crs_string)


# Create a map
m = folium.Map(location=[54, 15], tiles='openstreetmap', zoom_start=2)

# Add points to the map
for idx, row in universities.iterrows():
    Marker([row['Latitude'], row['Longitude']], popup=row['Name']).add_to(m)

# Display the map
m


world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
europe = world.loc[world.continent == 'Europe'].reset_index(drop=True)

europe_stats = europe[["name", "pop_est", "gdp_md_est"]]
europe_boundaries = europe[["name", "geometry"]]


europe_boundaries.head()


europe_stats.head()


# Use an attribute join to merge data about countries in Europe
europe = europe_boundaries.merge(europe_stats, on="name")
europe.head()


# Use spatial join to match universities to countries in Europe
european_universities = gpd.sjoin(universities, europe)

# Investigate the result
print("We located {} universities.".format(len(universities)))
print("Only {} of the universities were located in Europe (in {} different countries).".format(
    len(european_universities), len(european_universities.name.unique())))

european_universities.head()

We located 95 universities.
Only 90 of the universities were located in Europe (in 15 different countries).

<ipython-input-48-146764234e5d>:2: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.
Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.

Left CRS: +init=epsg:4326 +type=crs
Right CRS: EPSG:4326

  european_universities = gpd.sjoin(universities, europe)

#


import folium
from folium import Marker, GeoJson
from folium.plugins import HeatMap

import pandas as pd
import geopandas as gpd


releases = gpd.read_file("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/toxic_release_pennsylvania/toxic_release_pennsylvania/toxic_release_pennsylvania.shp") 
releases.head()


stations = gpd.read_file("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/PhillyHealth_Air_Monitoring_Stations/PhillyHealth_Air_Monitoring_Stations/PhillyHealth_Air_Monitoring_Stations.shp")
stations.head()


print(stations.crs)
print(releases.crs)

epsg:2272
epsg:2272


# Select one release incident in particular
recent_release = releases.iloc[360]

# Measure distance from release to each station
distances = stations.geometry.distance(recent_release.geometry)
distances

0     44778.509761
1     51006.456589
2     77744.509207
3     14672.170878
4     43753.554393
5      4711.658655
6     23197.430858
7     12072.823097
8     79081.825506
9      3780.623591
10    27577.474903
11    19818.381002
dtype: float64


print('Mean distance to monitoring stations: {} feet'.format(distances.mean()))

Mean distance to monitoring stations: 33516.28487007786 feet


print('Closest monitoring station ({} feet):'.format(distances.min()))
print(stations.iloc[distances.idxmin()][["ADDRESS", "LATITUDE", "LONGITUDE"]])

Closest monitoring station (3780.623590556444 feet):
ADDRESS      3100 Penrose Ferry Road
LATITUDE                    39.91279
LONGITUDE                 -75.185448
Name: 9, dtype: object


two_mile_buffer = stations.geometry.buffer(2*5280)
two_mile_buffer.head()

0    POLYGON ((2721944.641 257149.310, 2721893.792 ...
1    POLYGON ((2682494.290 271248.900, 2682443.441 ...
2    POLYGON ((2744886.638 280980.247, 2744835.789 ...
3    POLYGON ((2703638.580 233247.101, 2703587.731 ...
4    POLYGON ((2726959.773 251134.976, 2726908.924 ...
dtype: geometry


# Create map with release incidents and monitoring stations
m = folium.Map(location=[39.9526,-75.1652], zoom_start=11)
HeatMap(data=releases[['LATITUDE', 'LONGITUDE']], radius=15).add_to(m)
for idx, row in stations.iterrows():
    Marker([row['LATITUDE'], row['LONGITUDE']]).add_to(m)
    
# Plot each polygon on the map
GeoJson(two_mile_buffer.to_crs(epsg=4326)).add_to(m)

# Show the map
m


# Turn group of polygons into single multipolygon
my_union = two_mile_buffer.geometry.unary_union
print('Type:', type(my_union))

# Show the MultiPolygon object
my_union

Type: <class 'shapely.geometry.multipolygon.MultiPolygon'>


# The closest station is less than two miles away
my_union.contains(releases.iloc[360].geometry)

True


# The closest station is more than two miles away
my_union.contains(releases.iloc[358].geometry)

False

#


# Imports
import pandas as pd
from datetime import date, timedelta
import folium
from folium import Marker
from folium.plugins import MarkerCluster
import math
import matplotlib.pyplot as plt
import seaborn as sns

# Population Data
populationData = pd.read_csv('/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/archive (3)/2019_Census_US_Population_Data_By_State_Lat_Long.csv')
#/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/Europe

# Get the most recent date for filtering
freshDate = date.today() - timedelta(days=1)
freshDate = date.strftime(freshDate,"%Y%m%d")
freshDate = freshDate[0:4] + "-" + freshDate[4:6] + "-" + freshDate[6:8]

# Vaccination data, for most recent date
vaccinationData = pd.read_csv('https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/us_state_vaccinations.csv')
vaccinationByLocation = vaccinationData.loc[(vaccinationData.date == freshDate)][["location", "people_vaccinated"]]

# Vaccination and population data
vaccinationAndPopulationByLocation = pd.merge(populationData, vaccinationByLocation, left_on='STATE',right_on='location').drop(columns="location")

# Calculate percentage vaccinated by state
vaccinationAndPopulationByLocation["percent_vaccinated"] = vaccinationAndPopulationByLocation["people_vaccinated"] / vaccinationAndPopulationByLocation["POPESTIMATE2019"]

vaccinationAndPopulationByLocation


print("Date ran:", date.today())

# Calculate the total percent vaccinated in the US
percentageTotal = vaccinationAndPopulationByLocation["people_vaccinated"].sum() / vaccinationAndPopulationByLocation["POPESTIMATE2019"].sum()
print('Percentage Vaccinated in the US: {}%'.format(round(percentageTotal*100, 2)))

Date ran: 2022-05-03
Percentage Vaccinated in the US: 76.29%


# Create the map
v_map = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=4) 

# Add points to the map
mc = MarkerCluster()
for idx, row in vaccinationAndPopulationByLocation.iterrows(): 
    if not math.isnan(row['long']) and not math.isnan(row['lat']):
        mc.add_child(Marker(location=[row['lat'], row['long']],
                            tooltip=str(round(row['percent_vaccinated']*100, 2))+"%"))
v_map.add_child(mc)

# Display the map
v_map

#


#import pandas as pd
#import geopandas as gpd
#import gmaps
#import gmaps.datasets
#import matplotlib.pyplot as plt


#gmaps.configure(api_key='YOUR_GOOGLE_MAPS_API_KEY')


# unfinished - more code needed.

#


import pandas as pd
import geopandas as gpd
#import gmaps
#import gmaps.datasets
import matplotlib.pyplot as plt


#Directory where the shapefile is.
shapefile = "/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/Europe/Europe"
europa_gdf = gpd.read_file(shapefile + '.shp')
europa_gdf


europa_gdf = europa_gdf.drop("ORGN_NAME", axis = 1)
europa_gdf


europa_gdf.plot()

<AxesSubplot:>


percapita_df = pd.read_csv("/Users/db/Documents/Kaggle python tutorials/Geopandas/maps/Europe/percapita.csv")


europa_gdf["percapita"] = percapita_df["percapita"]
europa_gdf


europa_gdf.plot(column = "percapita", cmap = "gist_rainbow")

<AxesSubplot:>


fig = plt.figure(figsize=(9, 7))
ax = fig.add_subplot(111)

#Map title
title = "Europe Countries" "GDP Per Capita (USD)"

#Title plot with a line between the figur and the text
plt.title(title + "\n", fontsize=19)

#Map plot indicating what figure base (ax) has to take as reference
europa_gdf.plot(column="percapita", cmap = "gist_rainbow", ax = ax)

#Name of the horizontal y vertical axes
ax.set_xlabel("Longitude", fontsize = 13)
ax.set_ylabel("Latitude", fontsize = 13)

#Minumum and maximum values as limits of the color bar
min_p = min(europa_gdf["percapita"])
max_p = max(europa_gdf["percapita"])

#Definition of the graphhical bar
bar = plt.cm.ScalarMappable(cmap="gist_rainbow",
norm=plt.Normalize(vmin = min_p , vmax=max_p))

#It is declared a list where the values of the bar will be saved
bar._A = []

#Location and size of the bar
cax = plt.axes([0.85, 0.15, 0.03, 0.7]) #[xcoord, ycoord, width, long]

# Plot of the graphical bar with the upper variables (bar and cax)
cbar = fig.colorbar(bar, cax=cax)

#Bar title
cbar.set_label("GDP Per Capita (USD)", fontsize = 12)

#Saving of the map in a jpg file with an spicific resolution
plt.savefig("EuropaPIB.jpg", dpi = 200)

#

	OBJECTID	CATEGORY	UNIT	FACILITY	CLASS	UMP	DESCRIPTIO	REGION	COUNTY	URL	SOURCE	UPDATE_	OFFICE	ACRES	LANDS_UID	GREENCERT	SHAPE_AREA	SHAPE_LEN	geometry
0	1	FOR PRES DET PAR	CFP	HANCOCK FP DETACHED PARCEL	WILD FOREST	None	DELAWARE COUNTY DETACHED PARCEL	4	DELAWARE	http://www.dec.ny.gov/	DELAWARE RPP	5/12	STAMFORD	738.620192	103	N	2.990365e+06	7927.662385	POLYGON ((486093.245 4635308.586, 486787.235 4...
1	2	FOR PRES DET PAR	CFP	HANCOCK FP DETACHED PARCEL	WILD FOREST	None	DELAWARE COUNTY DETACHED PARCEL	4	DELAWARE	http://www.dec.ny.gov/	DELAWARE RPP	5/12	STAMFORD	282.553140	1218	N	1.143940e+06	4776.375600	POLYGON ((491931.514 4637416.256, 491305.424 4...
2	3	FOR PRES DET PAR	CFP	HANCOCK FP DETACHED PARCEL	WILD FOREST	None	DELAWARE COUNTY DETACHED PARCEL	4	DELAWARE	http://www.dec.ny.gov/	DELAWARE RPP	5/12	STAMFORD	234.291262	1780	N	9.485476e+05	5783.070364	POLYGON ((486000.287 4635834.453, 485007.550 4...
3	4	FOR PRES DET PAR	CFP	GREENE COUNTY FP DETACHED PARCEL	WILD FOREST	None	None	4	GREENE	http://www.dec.ny.gov/	GREENE RPP	5/12	STAMFORD	450.106464	2060	N	1.822293e+06	7021.644833	POLYGON ((541716.775 4675243.268, 541217.579 4...
4	6	FOREST PRESERVE	AFP	SARANAC LAKES WILD FOREST	WILD FOREST	SARANAC LAKES	None	5	ESSEX	http://www.dec.ny.gov/lands/22593.html	DECRP, ESSEX RPP	12/96	RAY BROOK	69.702387	1517	N	2.821959e+05	2663.909932	POLYGON ((583896.043 4909643.187, 583891.200 4...

	Region	District	FacilityName	Type	Town	Ownership	Latitude	Longitude	geometry
0	Ashanti	Offinso North	A.M.E Zion Clinic	Clinic	Afrancho	CHAG	7.40801	-1.96317	POINT (-1.96317 7.40801)
1	Ashanti	Bekwai Municipal	Abenkyiman Clinic	Clinic	Anwiankwanta	Private	6.46312	-1.58592	POINT (-1.58592 6.46312)
2	Ashanti	Adansi North	Aboabo Health Centre	Health Centre	Aboabo No 2	Government	6.22393	-1.34982	POINT (-1.34982 6.22393)
3	Ashanti	Afigya-Kwabre	Aboabogya Health Centre	Health Centre	Aboabogya	Government	6.84177	-1.61098	POINT (-1.61098 6.84177)
4	Ashanti	Kwabre	Aboaso Health Centre	Health Centre	Aboaso	Government	6.84177	-1.61098	POINT (-1.61098 6.84177)

	Region	District	FacilityName	Type	Town	Ownership	Latitude	Longitude	geometry
0	Ashanti	Offinso North	A.M.E Zion Clinic	Clinic	Afrancho	CHAG	7.40801	-1.96317	POINT (614422.662 818986.851)
1	Ashanti	Bekwai Municipal	Abenkyiman Clinic	Clinic	Anwiankwanta	Private	6.46312	-1.58592	POINT (656373.863 714616.547)
2	Ashanti	Adansi North	Aboabo Health Centre	Health Centre	Aboabo No 2	Government	6.22393	-1.34982	POINT (682573.395 688243.477)
3	Ashanti	Afigya-Kwabre	Aboabogya Health Centre	Health Centre	Aboabogya	Government	6.84177	-1.61098	POINT (653484.490 756478.812)
4	Ashanti	Kwabre	Aboaso Health Centre	Health Centre	Aboaso	Government	6.84177	-1.61098	POINT (653484.490 756478.812)

	Region	geometry
0	Ashanti	POLYGON ((-1.30985 7.62302, -1.30786 7.62198, ...
1	Brong Ahafo	POLYGON ((-2.54567 8.76089, -2.54473 8.76071, ...
2	Central	POLYGON ((-2.06723 6.29473, -2.06658 6.29420, ...
3	Eastern	POLYGON ((-0.21751 7.21009, -0.21747 7.20993, ...
4	Greater Accra	POLYGON ((0.23456 6.10986, 0.23484 6.10974, 0....

	Region	geometry	AREA
0	Ashanti	POLYGON ((686446.075 842986.894, 686666.193 84...	24379.017777
1	Brong Ahafo	POLYGON ((549970.457 968447.094, 550073.003 96...	40098.168231
2	Central	POLYGON ((603176.584 695877.238, 603248.424 69...	9665.626760
3	Eastern	POLYGON ((807307.254 797910.553, 807311.908 79...	18987.625847
4	Greater Accra	POLYGON ((858081.638 676424.913, 858113.115 67...	3706.511145

Kaggle First Map - setting up GeoPandas.¶

Get started with plotting in GeoPandas.¶

Coordinate Reference Systems¶

It's pretty amazing that we can represent the Earth's surface in 2 dimensions!¶

Interactive Maps¶

Learn how to make interactive heatmaps, choropleth maps, and more!¶

Manipulating Geospatial Data¶

Find locations with just the name of a place. And, learn how to join data based on spatial relationships.¶

Proximity Analysis¶

Measure distance, and explore neighboring points on a map.¶

US Vaccine Tracker¶

Python · 2019 Census US Population Data By State¶

Heatmap with Python and GoogleMaps Tutorial (gmaps)¶

https://parthsamin.medium.com/heatmap-with-python-and-googlemaps-tutorial-b3d623b77c95 ¶

Europe mapping¶

	INCIDENT_NUMBER	OFFENSE_CODE	OFFENSE_CODE_GROUP	OFFENSE_DESCRIPTION	DISTRICT	REPORTING_AREA	SHOOTING	OCCURRED_ON_DATE	YEAR	MONTH	DAY_OF_WEEK	HOUR	UCR_PART	STREET	Lat	Long	Location
0	I182070945	619	Larceny	LARCENY ALL OTHERS	D14	808	NaN	2018-09-02 13:00:00	2018	9	Sunday	13	Part One	LINCOLN ST	42.357791	-71.139371	(42.35779134, -71.13937053)
6	I182070933	724	Auto Theft	AUTO THEFT	B2	330	NaN	2018-09-03 21:25:00	2018	9	Monday	21	Part One	NORMANDY ST	42.306072	-71.082733	(42.30607218, -71.08273260)
8	I182070931	301	Robbery	ROBBERY - STREET	C6	177	NaN	2018-09-03 20:48:00	2018	9	Monday	20	Part One	MASSACHUSETTS AVE	42.331521	-71.070853	(42.33152148, -71.07085307)
19	I182070915	614	Larceny From Motor Vehicle	LARCENY THEFT FROM MV - NON-ACCESSORY	B2	181	NaN	2018-09-02 18:00:00	2018	9	Sunday	18	Part One	SHIRLEY ST	42.325695	-71.068168	(42.32569490, -71.06816778)
24	I182070908	522	Residential Burglary	BURGLARY - RESIDENTIAL - NO FORCE	B2	911	NaN	2018-09-03 18:38:00	2018	9	Monday	18	Part One	ANNUNCIATION RD	42.335062	-71.093168	(42.33506218, -71.09316781)

	geometry
DISTRICT
A15	MULTIPOLYGON (((-71.07416 42.39051, -71.07415 ...
A7	MULTIPOLYGON (((-70.99644 42.39557, -70.99644 ...
A1	POLYGON ((-71.05200 42.36884, -71.05169 42.368...
C6	POLYGON ((-71.04406 42.35403, -71.04412 42.353...
D4	POLYGON ((-71.07416 42.35724, -71.07359 42.357...

	Name
0	University of Oxford
1	University of Cambridge
2	Imperial College London
3	ETH Zurich
4	UCL

	Name	Latitude	Longitude	geometry
0	University of Oxford	51.750690	-1.247482	POINT (-1.24748 51.75069)
1	University of Cambridge	52.199852	0.119739	POINT (0.11974 52.19985)
2	Imperial College London	51.498959	-0.175641	POINT (-0.17564 51.49896)
3	ETH Zurich	47.408327	8.507564	POINT (8.50756 47.40833)
4	UCL	51.523581	-0.132977	POINT (-0.13298 51.52358)

	name	geometry
0	Russia	MULTIPOLYGON (((178.725 71.099, 180.000 71.516...
1	Norway	MULTIPOLYGON (((15.143 79.674, 15.523 80.016, ...
2	France	MULTIPOLYGON (((-51.658 4.156, -52.249 3.241, ...
3	Sweden	POLYGON ((11.027 58.856, 11.468 59.432, 12.300...
4	Belarus	POLYGON ((28.177 56.169, 29.230 55.918, 29.372...

	name	pop_est	gdp_md_est
0	Russia	142257519	3745000.0
1	Norway	5320045	364700.0
2	France	67106161	2699000.0
3	Sweden	9960487	498100.0
4	Belarus	9549747	165400.0

	YEAR	CITY	COUNTY	ST	LATITUDE	LONGITUDE	CHEMICAL	UNIT_OF_ME	TOTAL_RELE	geometry
0	2016	PHILADELPHIA	PHILADELPHIA	PA	40.005901	-75.072103	FORMIC ACID	Pounds	0.160	POINT (2718560.227 256380.179)
1	2016	PHILADELPHIA	PHILADELPHIA	PA	39.920120	-75.146410	ETHYLENE GLYCOL	Pounds	13353.480	POINT (2698674.606 224522.905)
2	2016	PHILADELPHIA	PHILADELPHIA	PA	40.023880	-75.220450	CERTAIN GLYCOL ETHERS	Pounds	104.135	POINT (2676833.394 261701.856)
3	2016	PHILADELPHIA	PHILADELPHIA	PA	39.913540	-75.198890	LEAD COMPOUNDS	Pounds	1730.280	POINT (2684030.004 221697.388)
4	2016	PHILADELPHIA	PHILADELPHIA	PA	39.913540	-75.198890	BENZENE	Pounds	39863.290	POINT (2684030.004 221697.388)

	SITE_NAME	ADDRESS	BLACK_CARB	ULTRAFINE_	CO	SO2	OZONE	NO2	NOY_NO	PM10	...	PAMS_VOC	TSP_11101	TSP_METALS	TSP_LEAD	TOXICS_TO1	MET	COMMUNITY_	LATITUDE	LONGITUDE	geometry
0	LAB	1501 East Lycoming Avenue	N	N	Y	N	Y	Y	Y	N	...	Y	N	Y	N	y	N	N	40.008606	-75.097624	POINT (2711384.641 257149.310)
1	ROX	Eva and Dearnley Streets	N	N	N	N	N	N	N	N	...	N	N	Y	N	Y	N	N	40.050461	-75.236966	POINT (2671934.290 271248.900)
2	NEA	Grant Avenue and Ashton Street	N	N	N	N	Y	N	N	N	...	N	N	N	N	N	Y	N	40.072073	-75.013128	POINT (2734326.638 280980.247)
3	CHS	500 South Broad Street	N	N	N	N	N	N	N	N	...	N	N	Y	N	Y	N	N	39.944510	-75.165442	POINT (2693078.580 233247.101)
4	NEW	2861 Lewis Street	N	N	Y	Y	Y	N	Y	Y	...	N	Y	N	Y	N	Y	N	39.991688	-75.080378	POINT (2716399.773 251134.976)

	STATE	POPESTIMATE2019	lat	long	people_vaccinated	percent_vaccinated
0	Alabama	4903185	32.377716	-86.300568	3075480.0	0.627241
1	Alaska	731545	58.301598	-134.420212	510725.0	0.698146
2	Arizona	7278717	33.448143	-112.096962	5318110.0	0.730638
3	Arkansas	3017804	34.746613	-92.288986	2012662.0	0.666929
4	California	39512223	38.576668	-121.493629	33093109.0	0.837541
5	Colorado	5758736	39.739227	-104.984856	4570138.0	0.793601
6	Connecticut	3565287	41.764046	-72.682198	3408122.0	0.955918
7	Delaware	973764	39.157307	-75.519722	809259.0	0.831063
8	District of Columbia	705749	38.895110	-77.036370	713148.0	1.010484
9	Florida	21477737	30.438118	-84.281296	17035838.0	0.793186
10	Georgia	10617423	33.749027	-84.388229	6937891.0	0.653444
11	Hawaii	1415872	21.307442	-157.857376	1237468.0	0.873997
12	Idaho	1787065	43.617775	-116.199722	1092082.0	0.611104
13	Illinois	12671821	39.798363	-89.654961	9719223.0	0.766995
14	Indiana	6732219	39.768623	-86.162643	4136805.0	0.614479
15	Iowa	3155070	41.591087	-93.603729	2143373.0	0.679342
16	Kansas	2913314	39.048191	-95.677956	2169971.0	0.744846
17	Kentucky	4467673	38.186722	-84.875374	2955530.0	0.661537
18	Louisiana	4648794	30.457069	-91.187393	2835880.0	0.610025
19	Maine	1344212	44.307167	-69.781693	1216113.0	0.904703
20	Maryland	6045680	38.978764	-76.490936	5227858.0	0.864726
21	Massachusetts	6892503	42.358162	-71.063698	6767555.0	0.981872
22	Michigan	9986857	42.733635	-84.555328	6687389.0	0.669619
23	Minnesota	5639632	44.955097	-93.102211	4233903.0	0.750741
24	Mississippi	2976149	32.303848	-90.182106	1773298.0	0.595836
25	Missouri	6137428	38.579201	-92.172935	4061666.0	0.661786
26	Montana	1068778	46.585709	-112.018417	696858.0	0.652014
27	Nebraska	1934408	40.808075	-96.699654	1359511.0	0.702805
28	Nevada	3080156	39.163914	-119.766121	2317905.0	0.752528
29	New Hampshire	1359711	43.206898	-71.537994	1197591.0	0.880769
30	New Jersey	8882190	40.220596	-74.769913	8027199.0	0.903741
31	New Mexico	2096829	35.682240	-105.939728	1838772.0	0.876930
32	North Carolina	10488084	35.780430	-78.639099	8831236.0	0.842026
33	North Dakota	762062	46.820850	-100.783318	494374.0	0.648732
34	Ohio	11689100	39.961346	-82.999069	7428524.0	0.635509
35	Oklahoma	3956971	35.492207	-97.503342	2812852.0	0.710860
36	Oregon	4217737	44.938461	-123.030403	3285372.0	0.778942
37	Pennsylvania	12801989	40.264378	-76.883598	10866570.0	0.848819
38	Rhode Island	1059361	41.830914	-71.414963	1043484.0	0.985013
39	South Carolina	5148714	34.000343	-81.033211	3465819.0	0.673143
40	South Dakota	884659	44.367031	-100.346405	676812.0	0.765054
41	Tennessee	6829174	36.165810	-86.784241	4245134.0	0.621617
42	Texas	28995881	30.274670	-97.740349	21205061.0	0.731313
43	Utah	3205958	40.777477	-111.888237	2311410.0	0.720973
44	Vermont	623989	44.262436	-72.580536	583312.0	0.934811
45	Virginia	8535519	37.538857	-77.433640	7314619.0	0.856962
46	Washington	7614893	47.035805	-122.905014	6150927.0	0.807750
47	West Virginia	1792147	38.336246	-81.612328	1164555.0	0.649810
48	Wisconsin	5822434	43.074684	-89.384445	4182104.0	0.718274
49	Wyoming	578759	41.140259	-104.820236	340391.0	0.588139

	NAME	ORGN_NAME	geometry
0	Albania	Shqipëria	MULTIPOLYGON (((19.50115 40.96230, 19.50563 40...
1	Andorra	Andorra	POLYGON ((1.43992 42.60649, 1.45041 42.60596, ...
2	Austria	Österreich	POLYGON ((16.00000 48.77775, 16.00000 48.78252...
3	Belgium	België / Belgique	POLYGON ((5.00000 49.79374, 4.99724 49.79696, ...
4	Bosnia Herzegovina	Bosna i Hercegovina	POLYGON ((19.22947 43.53458, 19.22925 43.53597...
5	Croatia	Hrvatska	MULTIPOLYGON (((14.30038 44.50156, 14.28972 44...
6	Czech Republic	Cesko	POLYGON ((14.82523 50.87399, 14.83687 50.86996...
7	Denmark	Danmark	MULTIPOLYGON (((11.99978 54.94118, 11.98534 54...
8	Estonia	Eesti	MULTIPOLYGON (((23.97511 58.09691, 23.96645 58...
9	Finland	Suomi	MULTIPOLYGON (((22.07310 60.22830, 22.06502 60...
10	France	France	MULTIPOLYGON (((-2.28137 46.68570, -2.31121 46...
11	Germany	Deutschland	MULTIPOLYGON (((13.11717 54.54924, 13.12529 54...
12	Gibraltar (UK)	Gibraltar (UK)	POLYGON ((-5.35322 36.15980, -5.34003 36.15975...
13	Greece	Elláda	MULTIPOLYGON (((24.74283 37.59560, 24.73534 37...
14	Guernsey (UK)	Guernsey (UK)	MULTIPOLYGON (((-2.52483 49.48867, -2.52034 49...
15	Hungary	Magyarország	POLYGON ((22.32725 48.36194, 22.32294 48.32583...
16	Ireland	Éire / Ireland	MULTIPOLYGON (((-10.21726 51.74543, -10.22042 ...
17	Isle of Man (UK)	Isle of Man (UK)	MULTIPOLYGON (((-4.38349 54.32594, -4.37470 54...
18	Italy	Italia	MULTIPOLYGON (((12.46117 37.88075, 12.45686 37...
19	Jersey (UK)	Jersey (UK)	POLYGON ((-2.03507 49.19905, -2.03699 49.19314...
20	Latvia	Latvija	POLYGON ((22.14997 57.64129, 22.17712 57.65100...
21	Liechtenstein	Liechtenstein	POLYGON ((9.53591 47.27354, 9.56037 47.24912, ...
22	Lithuania	Lietuva	MULTIPOLYGON (((26.22030 55.00000, 26.21693 54...
23	Luxembourg	Lëtzebuerg / Luxemburg / Luxembourg	POLYGON ((6.13802 50.13290, 6.14092 50.12927, ...
24	Macedonia	Makedonija	POLYGON ((22.37382 42.32667, 22.37228 42.32576...
25	Malta	Malta	MULTIPOLYGON (((14.33938 36.00751, 14.33490 36...
26	Monaco	Monaco	POLYGON ((7.40680 43.73196, 7.40175 43.73449, ...
27	Montenegro	Crna Gora	MULTIPOLYGON (((19.22947 43.53458, 19.22795 43...
28	Netherlands	Nederland	MULTIPOLYGON (((6.05039 53.44675, 6.04538 53.4...
29	Norway	Norge	MULTIPOLYGON (((14.82404 68.21677, 14.80662 68...
30	Poland	Polska	MULTIPOLYGON (((14.21288 53.86478, 14.21349 53...
31	Portugal	Portugal	MULTIPOLYGON (((-25.83763 37.88595, -25.83689 ...
32	San Marino	San Marino	POLYGON ((12.51004 43.99981, 12.51557 43.99566...
33	Serbia	Srbija	POLYGON ((18.83875 45.90742, 18.86463 45.91018...
34	Slovakia	Slovensko	POLYGON ((18.85061 49.52131, 18.85416 49.51941...
35	Slovenia	Slovenija	POLYGON ((16.56602 46.48372, 16.54178 46.48717...
36	Spain	España	MULTIPOLYGON (((-5.60904 36.00032, -5.61149 36...
37	Sweden	Sverige	MULTIPOLYGON (((16.69817 57.43244, 16.69456 57...
38	Switzerland	Schweiz / Suisse / Svizerra / Svizra	POLYGON ((9.53591 47.27354, 9.52660 47.25858, ...
39	United Kingdom	United Kingdom	MULTIPOLYGON (((-5.33523 51.85830, -5.34557 51...
40	Armenia	Hayastan	MULTIPOLYGON (((45.48185 40.65776, 45.49001 40...
41	Azerbaijan	Azerbaycan	MULTIPOLYGON (((49.02264 39.19647, 49.02892 39...
42	Belarus	Belarus	POLYGON ((30.00000 51.48946, 29.95314 51.48722...
43	Bulgaria	Balgarija	POLYGON ((22.68183 44.21765, 22.68898 44.21381...
44	Faeroe Islands (Denmark)	Foroyar (Danmark)	MULTIPOLYGON (((-6.37378 62.23575, -6.38222 62...
45	Georgia	Sakartvelo	POLYGON ((40.01024 43.38326, 40.01102 43.39860...
46	Iceland	Ísland	MULTIPOLYGON (((-15.38783 64.35684, -15.39691 ...
47	Jan Mayen (Norway)	Jan Mayen (Norge)	POLYGON ((-8.13727 71.14619, -8.09449 71.14722...
48	Moldova	Moldova	POLYGON ((27.72524 48.45466, 27.74713 48.45837...
49	Romania	România	MULTIPOLYGON (((29.59458 44.81385, 29.57739 44...
50	Svalbard (Norway)	Svalbard (Norge)	MULTIPOLYGON (((20.80218 78.56455, 20.80361 78...
51	Turkey	Türkiye	MULTIPOLYGON (((29.11932 40.83066, 29.11734 40...
52	Ukraine	Ukrajina	MULTIPOLYGON (((33.52988 45.87363, 33.52778 45...
53	Russia	Rossíya	MULTIPOLYGON (((52.65438 71.45161, 52.64216 71...

	NAME	geometry	percapita
0	Albania	MULTIPOLYGON (((19.50115 40.96230, 19.50563 40...	5847
1	Andorra	POLYGON ((1.43992 42.60649, 1.45041 42.60596, ...	42035
2	Austria	POLYGON ((16.00000 48.77775, 16.00000 48.78252...	50277
3	Belgium	POLYGON ((5.00000 49.79374, 4.99724 49.79696, ...	43814
4	Bosnia Herzegovina	POLYGON ((19.22947 43.53458, 19.22925 43.53597...	6727
5	Croatia	MULTIPOLYGON (((14.30038 44.50156, 14.28972 44...	15646
6	Czech Republic	POLYGON ((14.82523 50.87399, 14.83687 50.86996...	24569
7	Denmark	MULTIPOLYGON (((11.99978 54.94118, 11.98534 54...	63829
8	Estonia	MULTIPOLYGON (((23.97511 58.09691, 23.96645 58...	26377
9	Finland	MULTIPOLYGON (((22.07310 60.22830, 22.06502 60...	48461
10	France	MULTIPOLYGON (((-2.28137 46.68570, -2.31121 46...	44995
11	Germany	MULTIPOLYGON (((13.11717 54.54924, 13.12529 54...	51860
12	Gibraltar (UK)	POLYGON ((-5.35322 36.15980, -5.34003 36.15975...	50941
13	Greece	MULTIPOLYGON (((24.74283 37.59560, 24.73534 37...	18168
14	Guernsey (UK)	MULTIPOLYGON (((-2.52483 49.48867, -2.52034 49...	52531
15	Hungary	POLYGON ((22.32725 48.36194, 22.32294 48.32583...	15373
16	Ireland	MULTIPOLYGON (((-10.21726 51.74543, -10.22042 ...	0
17	Isle of Man (UK)	MULTIPOLYGON (((-4.38349 54.32594, -4.37470 54...	84600
18	Italy	MULTIPOLYGON (((12.46117 37.88075, 12.45686 37...	34997
19	Jersey (UK)	POLYGON ((-2.03507 49.19905, -2.03699 49.19314...	45320
20	Latvia	POLYGON ((22.14997 57.64129, 22.17712 57.65100...	19824
21	Liechtenstein	POLYGON ((9.53591 47.27354, 9.56037 47.24912, ...	143151
22	Lithuania	MULTIPOLYGON (((26.22030 55.00000, 26.21693 54...	22752
23	Luxembourg	POLYGON ((6.13802 50.13290, 6.14092 50.12927, ...	113196
24	Macedonia	POLYGON ((22.37382 42.32667, 22.37228 42.32576...	0
25	Malta	MULTIPOLYGON (((14.33938 36.00751, 14.33490 36...	32021
26	Monaco	POLYGON ((7.40680 43.73196, 7.40175 43.73449, ...	185741
27	Montenegro	MULTIPOLYGON (((19.22947 43.53458, 19.22795 43...	7933
28	Netherlands	MULTIPOLYGON (((6.05039 53.44675, 6.04538 53.4...	58003
29	Norway	MULTIPOLYGON (((14.82404 68.21677, 14.80662 68...	67987
30	Poland	MULTIPOLYGON (((14.21288 53.86478, 14.21349 53...	16930
31	Portugal	MULTIPOLYGON (((-25.83763 37.88595, -25.83689 ...	25065
32	San Marino	POLYGON ((12.51004 43.99981, 12.51557 43.99566...	44947
33	Serbia	POLYGON ((18.83875 45.90742, 18.86463 45.91018...	7497
34	Slovakia	POLYGON ((18.85061 49.52131, 18.85416 49.51941...	21529
35	Slovenia	POLYGON ((16.56602 46.48372, 16.54178 46.48717...	27452
36	Spain	MULTIPOLYGON (((-5.60904 36.00032, -5.61149 36...	31178
37	Sweden	MULTIPOLYGON (((16.69817 57.43244, 16.69456 57...	50339
38	Switzerland	POLYGON ((9.53591 47.27354, 9.52660 47.25858, ...	86673
39	United Kingdom	MULTIPOLYGON (((-5.33523 51.85830, -5.34557 51...	46344
40	Armenia	MULTIPOLYGON (((45.48185 40.65776, 45.49001 40...	4527
41	Azerbaijan	MULTIPOLYGON (((49.02264 39.19647, 49.02892 39...	4498
42	Belarus	POLYGON ((30.00000 51.48946, 29.95314 51.48722...	6133
43	Bulgaria	POLYGON ((22.68183 44.21765, 22.68898 44.21381...	11321
44	Faeroe Islands (Denmark)	MULTIPOLYGON (((-6.37378 62.23575, -6.38222 62...	58585
45	Georgia	POLYGON ((40.01024 43.38326, 40.01102 43.39860...	0
46	Iceland	MULTIPOLYGON (((-15.38783 64.35684, -15.39691 ...	57189
47	Jan Mayen (Norway)	POLYGON ((-8.13727 71.14619, -8.09449 71.14722...	0
48	Moldova	POLYGON ((27.72524 48.45466, 27.74713 48.45837...	4638
49	Romania	MULTIPOLYGON (((29.59458 44.81385, 29.57739 44...	14968
50	Svalbard (Norway)	MULTIPOLYGON (((20.80218 78.56455, 20.80361 78...	0
51	Turkey	MULTIPOLYGON (((29.11932 40.83066, 29.11734 40...	0
52	Ukraine	MULTIPOLYGON (((33.52988 45.87363, 33.52778 45...	3881
53	Russia	MULTIPOLYGON (((52.65438 71.45161, 52.64216 71...	11654

Kaggle First Map - setting up GeoPandas.¶

Get started with plotting in GeoPandas.¶

Coordinate Reference Systems¶

It's pretty amazing that we can represent the Earth's surface in 2 dimensions!¶

Interactive Maps¶

Learn how to make interactive heatmaps, choropleth maps, and more!¶

Manipulating Geospatial Data¶

Find locations with just the name of a place. And, learn how to join data based on spatial relationships.¶

Proximity Analysis¶

Measure distance, and explore neighboring points on a map.¶

US Vaccine Tracker¶

Python · 2019 Census US Population Data By State¶

Heatmap with Python and GoogleMaps Tutorial (gmaps)¶

https://parthsamin.medium.com/heatmap-with-python-and-googlemaps-tutorial-b3d623b77c95¶

Europe mapping¶

https://parthsamin.medium.com/heatmap-with-python-and-googlemaps-tutorial-b3d623b77c95 ¶